Abstract
An important quantity that is measured in NMR spectroscopy is the chemical shift. The interpretation of these data is mostly done by human experts. We present a method, named SimShiftDB, which identifies structural similarities between a protein of unknown structure and a database of resolved proteins based on chemical shift data. To evaluate the performance of our approach, we use a small but very reliable test set and compare our results to those of 123D and TALOS. The evaluation shows that SimShiftDB outperforms 123D in the majority of cases. For a significant part of the predictions made by TALOS, our method strongly reduces the error. SimShiftDB also assesses the startistical significance of each similarity identified. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Ginzinger, S. W., Gräupl, T., & Heun, V. (2007). SimShiftDB: Chemical-shift-based homology modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4414 LNBI, pp. 357–370). Springer Verlag. https://doi.org/10.1007/978-3-540-71233-6_28
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